Topological Data Analysis (TDA) has emerged recently as a robust tool to extract and compare the structure of datasets. TDA identifies features in data such as connected components and holes and assigns a quantitative measure to these features. Several studies reported that topological features extracted by TDA tools provide unique information about the data, discover new insights, and determine which feature is more related to the outcome. On the other hand, the overwhelming success of deep neural networks in learning patterns and relationships has been proven on a vast array of data applications, images in particular. To capture the characteristics of both powerful tools, we propose \textit{TDA-Net}, a novel ensemble network that fuses topological and deep features for the purpose of enhancing model generalizability and accuracy. We apply the proposed \textit{TDA-Net} to a critical application, which is the automated detection of COVID-19 from CXR images. The experimental results showed that the proposed network achieved excellent performance and suggests the applicability of our method in practice.
翻译:最近,地形数据分析(TDA)成为提取和比较数据集结构的有力工具。TDA查明了数据中的特征,例如连接部件和孔,并指定了对这些特征的定量测量。一些研究报告说,TDA工具提取的地形特征提供了有关数据的独特信息,发现了新的洞察力,并确定了哪些特征与结果更为相关。另一方面,深层神经网络在学习模式和关系方面的巨大成功在大量数据应用中得到了证明,特别是图像。为了捕捉这两个强大的工具的特征,我们提议了\textit{TDA-Net},这是一个新型的连带网络,将表层和深度特征结合起来,以加强模型的通用性和准确性。我们将提议的\textit{TDA-Net}应用于一个关键应用程序,即从CXR图像中自动检测COVID-19。实验结果表明,拟议的网络取得了出色的业绩,并表明我们的方法在实践中的适用性。